Vol 2, No 1







In Publishing

Table of Contents

Articles

by Yi-Sheng Wang, Yu-Zhan Lu
97 Views, 52 PDF Downloads

The purpose of this study is to explore in depth the special context and unique experience of the live video streaming and to provide insights regarding an interpretation of the contextualization experiences model. This study uses grounded theory, depth interviews, and the physical travel of researchers to the field for participation and observations. Finally, the insight of the live broadcast platform contextualization was developed. The theoretical contribution of this study is to establish the words of mouth relationship of the live broadcast platform and ten related propositions. The study revealed the mystery of live video streaming.

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Articles

by Lixiu Hao, Weiwei Yu
37 Views, 8 PDF Downloads

Objective Face recognition can be affected by unfavorable factors such as illumination, posture and expression, but the face image set is a collection of people’s various angles, different illuminations and even different expressions, which can effectively reduce these adverse effects and get higher face recognition rate. In order to make the face image set have higher recognition rate, a new method of combining face image set recognition is proposed, which combines an improved Histogram of Oriented Gradient (HOG) feature and Convolutional Neural Network (CNN). Method The method firstly segments the face images to be identified and performs HOG to extract features of the segmented images. Secondly, calculate the information entropy contained in each block as a weight coefficient of each block to form a new HOG features, and non-negative matrix factorization (NMF) is applied to reduce HOG features. Then the reduced-dimensional HOG features are modeled as image sets which keep your face details as much as possible. Finally, the modeled image sets are classified by using a convolutional neural network. Result The experimental results show that compared with the simple CNN method and the HOG-CNN method, the recognition rate of the method on the CMU PIE face set is increased by about 4%~10%. Conclusion The method proposed in this paper has more details of the face, overcomes the adverse effects, and improves the accuracy.

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